Here are reputable websites that offer AI / ML project tutorials, with a quick note about what you’ll find on each and example project types to try.

• Kaggle (Kaggle.com) — hands-on notebooks, datasets, and competitions. Great for end-to-end projects: tabular ML, image classification, NLP, time series.
• GitHub (GitHub.com) — search repositories and curated project lists (awesome-ml, etc.). Good for reproducible code, full projects, and deployment examples.
• Papers with Code (paperswithcode.com) — links papers to code and leaderboards. Use it to reproduce state-of-the-art models and find implementation-ready projects.
• Hugging Face (huggingface.co) — transformers, datasets, end-to-end NLP/ multimodal tutorials and community model hub. Try fine-tuning language models, building chatbots, or multimodal apps.
• TensorFlow Tutorials (TensorFlow.org/tutorials) — official guides for TensorFlow/Keras covering image, text, time series, and TF Lite deployment.
• PyTorch Tutorials (PyTorch.org/tutorials) — official PyTorch guides and example projects including vision, NLP, and production workflows.
• fast.ai (course.fast.ai) — practical deep-learning courses with project-focused labs; great for rapid prototyping and transfer learning projects.
• DeepLearning.AI (DeepLearning.AI / via Coursera) — project-centered specialization courses (including those by Andrew Ng) with capstone projects.
• Coursera (Coursera.org) — many AI/ML courses with guided projects and peer-graded capstones from universities and industry.
• Udacity (Udacity.com) — Nanodegree programs with real-world AI projects and mentor reviews (computer vision, NLP, ML engineering).
• Microsoft Learn (learn.microsoft.com) — step-by-step AI modules and end-to-end deployment tutorials using Azure and open-source frameworks.
• Google Cloud Tutorials (cloud.Google.com/learn) — practical ML/AI projects, MLOps, and deployment on Google Cloud; includes Colab notebooks.
• Google Colab Examples (colab.research.Google.com) — shared notebooks for quick experiments and tutorials (many community examples).
• Towards Data Science (towardsdatascience.com / Medium) — many step-by-step project walkthroughs and code snippets for practical AI projects.
• Analytics Vidhya (analyticsvidhya.com) — beginner-to-intermediate project tutorials for ML, NLP, and computer vision.
• DataCamp Projects (DataCamp.com/projects) — guided, interactive coding projects for applied ML and data science.
• YouTube channels (3Blue1Brown, Siraj Raval, Two Minute Papers, Yannic Kilcher, Henry AI Labs) — many project walkthroughs and explanations; often include links to code.

Tips for choosing tutorials:

• Pick projects with datasets and code (not just theory).
• Start with small end-to-end projects (data → model → evaluation → simple deployment).
• Reproduce an existing project first, then modify it (new data, model tweaks, or deployment).
• Use Colab or a cloud GPU if you don’t have a local GPU.
• Track progress with a GitHub repo and write a short README or blog post — great for your portfolio.

If you want, I can recommend 5 concrete project tutorials (with links and starter notebooks) matched to your skill level and interests (NLP, vision, or tabular). Which focus would you prefer?